Sentence examples for multiple knowledge sources from inspiring English sources

In this paper, we introduce a knowledge-engineering methodology to build a generic, reusable resource monitoring system for industrial plants which includes multiple knowledge sources.

Multiple knowledge sources are then reasonably integrated via an early fusion of normalized noisy features with multiple beamforming techniques, enhanced speech features, speaker-related features, and other auxiliary features concatenated as the input to each subnet to compensate for imperfect front-end processing.

As this class was correctly populated with 88 instances of chemical entities satisfying the Rule of Five, it became apparent that consistent molecular identifiers permitted the effective collapse of the multiple knowledge sources to provide the information necessary in order to fulfil the classification and identify drug-like chemical entities.

Therefore, in the present study, we propose a coexpression based method which incorporates multiple knowledge sources to find relevant target genes.

In the final model, where we used multiple knowledge sources, HIF-1 signaling from KEGG, the GO term "Cell surface binding", and the Pfam "Leucine rich repeat N-terminal domain" were selected.

The prototype web-based collaborative design platform, is implemented for proof-of-concept through the distributed knowledge server based framework (KS-DMME) with concurrent integration of multiple cooperative knowledge sources and software using Java/Java Bean and CORBA technologies.

As such, the assembly-oriented design system can easily be implemented under the multi-agent-based knowledge-intensive Petri net framework with concurrent integration of multiple cooperative knowledge sources and software.

It combines information from multiple public knowledge sources such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Database of Interacting Proteins (DIP) and the Protein Families Database (Pfam) [ 29].

Authors Kelm, P., Schmiedeke S., Choi J., Friedland G., Ekambaram V., Ramchandran K., & Sikora T. This article describes a novel fusion approach using multiple modalities and knowledge sources that improves the accuracy of multimodal location estimation algorithms.

The advantage of using these multiple prior-knowledge sources was shown by Hecker et al.[ 2].

A better integration of multiple scales, different societal actors, various knowledge sources, and diverse disciplines into disaster risk research will increase its relevance for decision-makers in policy and practice.